Engineer Features for Consultant Attrition

Business Context

Collabera wants to predict whether a consultant will become attrition-risk in the next 60 days so account managers can intervene early. You are given historical consultant-level data from Collabera Talent Solutions and must design a feature engineering pipeline that improves model quality while remaining explainable to operations teams.

Dataset

• Size: 142K consultant-month records across 24 months, 45 raw columns
• Target: Binary label indicating whether the consultant exits or becomes unassigned within 60 days
• Class balance: 11.6% positive, 88.4% negative
• Missing data: 18% missing in engagement fields, 7% in payroll fields, and sparse values for newly onboarded consultants

Success Criteria

A strong solution should improve performance over a raw-feature baseline and reach ROC-AUC >= 0.82 and PR-AUC >= 0.42 on a held-out time-based test set. The feature set should also support clear explanation of the top risk drivers.

Constraints

• Use only information available up to the prediction date; avoid leakage from future assignment outcomes.
• Batch scoring must finish nightly for ~35K active consultants.
• The final feature pipeline should be reproducible and deployable in Collabera's scheduled ML workflow.
• Prefer interpretable engineered features over opaque embeddings.

Deliverables

1. Define a feature engineering plan for numerical, categorical, and temporal fields.
2. Build a training pipeline with preprocessing, feature generation, and a classification model.
3. Compare at least one baseline model against a feature-engineered model.
4. Explain how you prevent leakage and validate using time-based splits.
5. Report evaluation metrics and identify the most useful engineered features.